Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,787
2	New Jersey	18,615
3	Massachusetts	15,111
4	Rhode Island	14,873
5	District of Columbia	13,513
6	Connecticut	12,438
7	Illinois	10,329
8	Delaware	10,326
9	Maryland	9,943
10	Louisiana	9,495
11	Nebraska	8,284
12	Iowa	7,205
13	Michigan	6,546
14	Pennsylvania	6,359
15	South Dakota	6,334
16	Mississippi	6,210
17	Virginia	6,112
18	Indiana	5,837
19	Minnesota	5,124
20	Colorado	4,946
21	Georgia	4,847
22	Alabama	4,484
23	New Mexico	4,411
24	Tennessee	4,155
25	Arizona	4,118
26	Utah	4,049
27	North Dakota	3,863
28	New Hampshire	3,808
29	Kansas	3,752
30	Wisconsin	3,739
31	North Carolina	3,652
32	California	3,546
33	Arkansas	3,435
34	Washington	3,406
35	Ohio	3,385
36	Nevada	3,316
37	Florida	3,136
38	South Carolina	3,060
39	Texas	2,820
40	Kentucky	2,692
41	Missouri	2,551
42	Maine	1,961
43	Oklahoma	1,890
44	Idaho	1,825
45	Vermont	1,754
46	Wyoming	1,693
47	Puerto Rico	1,668
48	West Virginia	1,223
49	Oregon	1,208
50	Alaska	877
51	Montana	524
52	Hawaii	475

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	139
2	Mississippi	135
3	Arkansas	104
4	Alabama	101
5	Iowa	99
6	Rhode Island	99
7	District of Columbia	96
8	South Carolina	95
9	Utah	93
10	Louisiana	87
11	Maryland	83
12	North Carolina	83
13	Tennessee	76
14	Illinois	74
15	California	68
16	Nebraska	67
17	Texas	66
18	Georgia	65
19	South Dakota	62
20	Minnesota	59
21	Nevada	58
22	Virginia	58
23	Florida	53
24	New Mexico	49
25	Indiana	47
26	Kentucky	47
27	Wisconsin	47
28	New Jersey	44
29	Kansas	43
30	Delaware	39
31	Washington	38
32	Missouri	36
33	Pennsylvania	36
34	Connecticut	35
35	New York	35
36	Puerto Rico	35
37	Massachusetts	34
38	North Dakota	34
39	New Hampshire	33
40	Ohio	31
41	Colorado	28
42	Oklahoma	27
43	Michigan	25
44	Alaska	24
45	Oregon	22
46	Idaho	20
47	Wyoming	19
48	Vermont	17
49	Maine	16
50	West Virginia	9
51	Montana	4
52	Hawaii	2

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,561
2	New Jersey	1,393
3	Connecticut	1,155
4	Massachusetts	1,081
5	Rhode Island	766
6	District of Columbia	707
7	Louisiana	638
8	Michigan	596
9	Illinois	497
10	Pennsylvania	479
11	Maryland	470
12	Delaware	424
13	Indiana	349
14	Mississippi	291
15	Colorado	273
16	Minnesota	224
17	New Hampshire	221
18	Georgia	215
19	Ohio	210
20	Iowa	202
21	New Mexico	195
22	Virginia	177
23	Washington	155
24	Alabama	151
25	Arizona	151
26	Nevada	145
27	Missouri	140
28	Florida	130
29	California	123
30	Wisconsin	115
31	Kentucky	111
32	South Carolina	111
33	Nebraska	108
34	North Carolina	104
35	North Dakota	99
36	Oklahoma	89
37	Vermont	88
38	Kansas	82
39	South Dakota	77
40	Maine	74
41	Texas	66
42	Tennessee	63
43	Arkansas	54
44	Idaho	47
45	West Virginia	47
46	Puerto Rico	44
47	Oregon	40
48	Utah	39
49	Wyoming	31
50	Montana	16
51	Alaska	12
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	12
2	Illinois	9
3	New Jersey	7
4	Massachusetts	6
5	Delaware	5
6	Maryland	5
7	Mississippi	5
8	Connecticut	4
9	District of Columbia	4
10	Georgia	4
11	Minnesota	4
12	Alabama	3
13	Iowa	3
14	New Hampshire	3
15	New York	3
16	Pennsylvania	3
17	Arizona	2
18	Colorado	2
19	Indiana	2
20	Louisiana	2
21	Michigan	2
22	Missouri	2
23	Nebraska	2
24	New Mexico	2
25	North Carolina	2
26	Ohio	2
27	Arkansas	1
28	California	1
29	Florida	1
30	Kentucky	1
31	Nevada	1
32	South Carolina	1
33	South Dakota	1
34	Tennessee	1
35	Virginia	1
36	Wisconsin	1
37	Alaska	0
38	Hawaii	0
39	Idaho	0
40	Kansas	0
41	Maine	0
42	Montana	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,804	1	99
Lake	Tennessee	101,482	2	99
Dakota	Nebraska	85,938	3	99
Lincoln	Arkansas	75,630	4	99
Nobles	Minnesota	73,929	5	99
Davidson	Tennessee	9,466	244	92
Richland	South Carolina	4,661	627	80
Pierce	Washington	2,612	1079	65
Orange	California	2,516	1126	64
York	South Carolina	1,908	1388	55

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,429	1	99
Terrell	Georgia	3,048	2	99
Early	Georgia	3,042	3	99
Randolph	Georgia	2,951	4	99
Northampton	Virginia	2,306	5	99
Richland	South Carolina	168	646	79
Davidson	Tennessee	115	813	74
Pierce	Washington	96	912	70
Orange	California	62	1153	63
York	South Carolina	36	1441	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.